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Lean Six Sigma Trends

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Rapid process improvement methods

The early training models for Six Sigma black belt project execution required between 4 and 6 months of advanced training that moved belts through the Measure, Analyze, Improve and Control phases (MAIC).   The Define phase was later introduced by General Electric in the late 1990's as the program was deployed across the world. Project work was completed between the training weeks.  This training model required a very long cycle time because advanced statistical modeling tools had to be taught in a strict sequence.


As deployments moved into service applications, the tools required to identify and execute projects focused more on value identification, waste removal (non-value adding) as well as process simplification and standardization. Some organizations remain trapped in a low value phased deployment model that produces process improvements only after many months or years. Sometimes this is a necessary strategy; but, not for most project applications.


A rapidly emerging trend is to create a balanced blend of Lean, Six Sigma, Kaizen and operational assessment methods (management consulting) to enable belts to quickly understand basic process improvement methods. This strategy helps them identify, prioritize and organize projects for solution.  Projects can be completed more quickly to rapidly increase productivity in organizations of many types. A key attribute of this rapid improvement strategy is a focus on highly interactive simulations and relevant breakouts that reinforce an organization’s goals and priorities.


Financial analysis for process Improvement

From my book titled, Operational Excellence-Using Lean Six Sigma to Translate Customer Value through Global Supply Chains (Chapters 7 and 17) The basis for quality cost analysis, rationalization and implementation is Dr. Joseph Juran who advocated more than 50 years ago that the elimination of quality failures e.g. warranty, and returned goods expense, the reduction of internal appraisal costs e.g. inspection and corrective actions would provide resources for preventive actions. This was the most effective strategy for justifying process improvement activities. He suggested two complementary strategies for process improvement i.e. continuous versus breakthrough initiatives. The Lean Six Sigma (LSS) program was designed to identify and execute breakthrough improvements whereas continuous improvement programs when properly aligned are essential for long term cultural transformation.

In the early days of the AlliedSignal (now Honeywell)  Operational Excellence initiative, LSS required that project benefits be tied to hard financial savings targets. The goal was to avoid “science projects”. The benefits were then aligned to return-on-equity (ROE) targets and translated to operational measures. One example would be increasing cash flow by reducing inventory investment using efficient asset utilization strategies. These projects were critical for driving productivity. Financial representatives verified the project savings; savings were “calendarized” rather than annualized. The concept works this way: if a project’s annual savings is $100,000 and it is implemented on July 1st then its calenderized savings is $50,000.  This forces rapid closure of projects. Other types of financial benefits were also tracked. These included cost avoidance, revenue increases and more effective asset utilization which increased cash flow and lowered interest and other expenses. To provide a balanced approach to benefit estimation, non-financial benefits were also allowed; but, these were always intended to be a small part of the benefits.

AlliedSignal, like many organizations that deployed continuous improvement in the 1980’s did realize significant benefits using the LSS initiative. LSS brought rapid and effective project execution. That is why more than twenty years later it remains strong in organizations that effectively deploy it. Unfortunately, it is also is in rapid decline in organizations where it is not effectively deployed. Ineffective deployments are characterized by poor project alignment, lack of sponsorship as well as ineffective training and coaching. An effective LSS deployment should create between 1% and  2% year over year productivity. Twenty years ago at AlliedSignal we did ~4%. That was leadership.

Organizational change in a competitive world

Organizational change occurs if a majority of people within an organization consistently practice a new behavior. This requires that they are convinced the new behaviors provide value. The need for organizational change could be internal or external threats i.e. "a burning platform". Examples include bankruptcy, a forced merger, lost customers or other factors. However, even in extreme circumstances, organizational change is incremental. Research shows that a large scale change program often requires between five and twenty years to translate into new behaviors as measured by performance metrics. Execution type cultures tend to react more quickly and effectively than those not aligned with metrics.

 

From my book titled, Operational Excellence-Using Lean Six Sigma to Translate Customer Value through Global Supply Chains, successful organizations apply proven paradigms and behaviors. Unless an organization sees value from changing its current “way of doing things” it will not embrace a new change program. There are proven principles that help implement change.  According to John P. Kotter in his book titled, “Leading Change, there are eight key characteristics of a successful change initiative. In addition to these eight key characteristics, organizations should identify the key success metrics needed to evaluate change effectiveness. Examples include the percentage of people effectively using new tools and methods and cumulative business benefits by type. Business benefits should be planned top down and measured at a project level and then aggregated up through an organization to ensure alignment.  Finally, any barriers inhibiting a program's deployment should be identified and eliminated 


Top challenges facing CEOs

The Lean Six Sigma program helps execute business strategies with other programs such as supply chain excellence, design excellence, Total Customer Experience (TCE) and others.  LSS must be aligned with both other initiatives and executive goals. Lean Six Sigma (LSS) practitioners are often asked what leading trends are influencing the initiative today. But, it is more useful to understand what  executives of leading organizations think of current trends and then to identify ways LSS can be effectively integrated.


A consolidated list of these CEO challenges include security risks around cloud (virtual) storage mobile devices and infrastructure of many types, technology changes including big and small data analytics, social media promotion and effective usage, regulations of many types across the world, social activism, employee culture and retention (Millennials outnumber baby boomers) and skill shortages, talent recruitment, the creation of technology innovations to drive revenue growth, competing in a world where there is hyper-competition and constant business model disruption. There are many other challenges too.


How can the tools and methods of Lean Six Sigma help organizations in the coming years? First, the core competency of LSS is its process centric view of metric base-lining, root cause analysis and breakthrough improvement across processes with poor performance.  Currently, LSS  methods are routinely applied to improve employee recruitment and retention, the satisfaction of regulatory requirements as well as analytics. Still to be developed are large and small data applications, social media effectiveness, customer loyalty enhancement, social action (sustainability, health, emergency response etc.) and others. The development of new products and services can also be accelerated through integration of Design-for-Six Sigma (DFSS) methods, road-maps to drive revenue growth and total customer experience. Hyper-competition and business model disruptions use cases are varied; but, organizations can be helped by LSS, to an extent, through end-to-end brainstorming workshops and risk mitigation analyses. 



What is Black Belt certification?

After more than twenty years, the paths towards belt certification vary across providers and organizations.  The original intent for “belts” was the practical application of quality tools and methods to create breakthrough process improvement. In the 1980’s (and earlier) Dr. Joseph Juran in his book  titled, “Juran’s Quality Handbook, discussed the concept of continuous versus breakthrough improvement projects. Juran’s breakthrough concept was the basis for Motorola’s Six Sigma program in the 1980’s which produced significant process improvements and business benefits. The enormous success of the Six Sigma program, which was positioned in the mid-1990's by AlliedSignal and General Electric, is directly attributable to the significant business benefits created through the many projects deployed around the world in the past twenty years.


In the mid-1990’s, black belt certification was well defined. Training was delivered to enable a belt to identify and execute breakthrough improvement projects that were sponsored by the organization’s senior management. Sponsorship ensured project alignment with business goals. The trainer i.e. the master black belt coached belts towards project completion and certification.  Belts needed to understand the theory and apply it practically to solve problems. Through the late 1990’s and into the first decade of the 21st century, the number of trained belts increased as did the 3rd party providers. Certification criteria began to slip.


The basics for success have not changed. The right tools and methods are used to work through applied projects that yield business benefits. It is important to differentiate between training versus certification competencies. Certification requires the completion of beneficial projects.  In summary, business benefits are the real measure of deployment success. Not the number of belts trained or the number of simulations and science projects that are delivered to an organization. Experienced coaching is also important as well as relevant training content and delivery. In the end, each “belt” is responsible for his or her professional development. This only occurs when “belts” practice their skills through practical project application.    

 

Translating customer surveys into improvement projects

Customer surveys have become very popular in recent years. Their usage accelerated since Frederick F. Reichheld’s 2003 publication of the Harvard Business article titled, “The One Number You Need to Grow”. A quotation from the article states, “By substituting a single question for the complex black box of the typical customer satisfaction survey, companies can actually put consumer survey results to use and focus employees on the task of stimulating growth.”  One key point from the article is that truly loyal customers tend to buy more over time and as their incomes grow they spend more with companies they feel good about. Their research showed one question in particular, “How likely is it that you would recommend [company X] to a friend or colleague?” was good predictor of repeat purchasing. The research team found three logical clusters: “Promoters,” the customers with the highest rates of repurchase and referral, gave ratings of nine or ten to the question. The “passively satisfied” logged a seven or an eight, and “detractors” scored from zero to six.” The net promoter score (NPS) which is the % promoters minus % detractors (ignoring neutrals) was calculated. A subsequent exhaustive analysis of more than 400 companies over 12 industries confirmed the NPS predictive model.


As process improvement experts, our interest is the translation of the NPS and loyalty metrics into actionable improvement projects. Currently there are analytical gaps. The central problem is that the professionals designing surveys are not process improvement experts and process improvement experts do not design surveys. This result is that survey information is not sufficient to create project baselines to initiate root cause analyses. The effect is that customers become fatigued or lose interest in surveys that continually regurgitate similar questions without effective resolution. A fact is that most belts are not trained in logistics regression and other advanced methods that are necessary to create analytical models to translate the voice-of-the customer (VOC) obtained through surveys into actionable process improvement projects.


Complexity is a major cause of system failure

From my book titled, Unexpected Consequences, a phenomenon called “diffusion of responsibility” occurs in situations where there are many people; but, without clearly defined roles and responsibilities. In these situations, each person thinks someone else will take responsibility, but, no one does. This phenomenon is dependent on group size Interestingly, people who witness such events would take action if by themselves or in the company of only a few people. In these latter situations, personal responsibility is known and easily communicated between individuals.  Diffusion of responsibility also occurs in organizations and work teams with poorly defined roles and responsibilities. It is one of several cognitive influences affecting how people work and make decisions. It with other cognitive factors e.g. group attitudes and behaviors, organizational culture and ethics influences how things are designed, used and fail in the context of available technologies. Attitudes and behaviors have a significant influence when creating and using products and services and are often causal factors of catastrophic failures.

 

People directly and indirectly influence the complexity of products as well as services and systems used in design, production and distribution. Complexity results from combinations of cognitive, group and organizational influences in association with technologies and their use. Albert Einstein said, “Common sense is the collection of prejudices acquired by age eighteen.”  People accumulate prejudices e.g. biases which distort the information they perceive, remember and use.  Some of the things we think are true are not. Complexity creates a fog which makes it  difficult  to see risks and consequences. This increases the likelihood of failure. The Encarta Dictionary defines it as, “the condition of being difficult to analyze, understand, or solve ….the condition of being made up of many interrelated parts”.  It occurs when   people, machines, equipment and management systems are brought together and coordinated for work or other activities.  Complexity is also exacerbated by great distance, unfamiliar cultures, different languages, and the use of leading edge technologies, the types of work performed, the machines and equipment employed and the application environments in which these things are used by people, machines and systems. Depending on the alignment of risks e.g. recurrence and other types, the resultant failures may be catastrophic.

 

The effects of complexity are seen as undocumented work activities, poorly trained workers, little or no performance measurements, dangerous working environments e.g. fatigue, “near misses” almost causing failure, frequent changes to work procedures and complicated instructions and controls. There are many others. Complexity overwhelms people creating conditions in which the easiest activities are difficult for lack of time and resources. Energy levels alternate between low levels if overwhelmed to high levels because of uncontrolled stress. Poor prioritization of work activities is another characteristic of complex work environments. Nothing seems to get done or the wrong things get done.  An obvious sign complexity has overwhelmed a system is catastrophic failure.

 

What are some solutions? Simplification, standardization and mistake proofing are effective preventive strategies, but, they may not be enough. There are other effective tools and methods for helping reduce complexity. But, depending on the system, catastrophic failures may still occur. A major reason for failures is an over reliance on technology rather than an understanding of social psychological influences. Important details maybe lost when translating customer requirements to design products, services, buildings, infrastructure, logistical and other systems. Systems routinely fail for non-technical reasons e.g. airplane accidents caused by pilot’s perceptual errors, fatigue or poor maintenance.  A higher number of deaths, injuries and damage to property and the environment are caused by human and organizational errors and mistakes rather than technology.

 

A short history of the AlliedSignal Six Sigma deployment

From my book titled, Operational Excellence-Using Lean Six Sigma to Translate Customer Value through Global Supply Chains, the Six Sigma program focuses onbreakthrough” as opposed to more gradual continuous process improvements. It started at Motorola in response to competitive threats to its consumer  electronics  business. Based on a need to significantly improve productivity, Richard Schroeder, the VP of Quality, a former Motorola executive and Larry Bossidy the CEO of AlliedSignal and former General Electric executive, deployed the program in late 1994. It was driven by external consultants and aggressive change agents.


The Six Sigma, Lean and Total Productive Maintenance (TPM) programs quickly became top productivity drivers at AlliedSignal contributing, between 2% and 4% year-over-year productivity in addition to other improvement initiatives such as reengineering activities. These operational initiatives were folded into AlliedSignal’s Operational Excellence program in late 1995. In 1996, the Six Sigma program was also deployed at General Electric (GE). By 1997, several other major organizations began to deploy Six Sigma and the program diffused across the world. The effectiveness of the program is undisputable to those who have been part of it’s many successful deployments. But, some organizations developed ineffective versions of the program or it fell into disuse for various reasons. 


Key Six Sigma success factors are the alignment of the program with an organization’s strategic business goals and objectives, guidance by an executive steering committee, the identification of projects to increase productivity and the selection of full-time and high caliber “belts” trained to execute the project portfolio. A successful Lean Six Sigma deployment for organizations over one billion dollars should provide productivity increases. The tools and methods incorporated into the deployment’s strategies, tactics and the project execution actions are well known and proven i.e. the DMAIC roadmap that will provide effective guidance for process improvement. 



Why are Lean Six Sigma operational assessments important?

From my book titled, Operational Excellence-Using Lean Six Sigma to Translate Customer Value through Global Supply Chains, operational assessments help identify and align improvement projects for a Lean Six Sigma deployment. A well done assessment will provide documented business benefits that ensure projects are integrated with an organization’s business goals while recognizing resource and other constraints. Assessment activities include conducting cultural surveys, gathering operational and financial data of important workflows and developing the deployment plan. A critical link between an assessment and its identified business benefits are well defined and financially justified project charters.

Two key activities are necessary in the early stages of an assessment. The first requires working with an organization’s executives to ensure strategic goals align across the local assessment teams. This requires higher level financial and operational metrics are successively delayered to a project charter level to positively influence productivity and Total Customer Experience (TCE). Executive training is deployed prior to deploying assessment teams. This is a facilitated discussion to identity higher level financial and operational performance gaps that should be investigated during the assessment. A critical-to flow down method is used to identify areas of opportunity. Each assessment team ensures consensus is reached with the local work teams and their management regarding opportunities. An assessment report provides local management with the key findings gained from the assessment. Integral components of the assessment report include quantitative analyses of financial and operational reports as well as analyses of major process workflows. This information is used to create project charters that are not “ideas or abstractions” but rather well defined and financially justified within the context of senior executive strategy.  Anticipated barriers to the implementation of the Lean Six Sigma program are also discussed in the assessment report as well as required resources for the deployment. 



How is Big Data Analytics different from Lean Six Sigma?

 

From my book titled, Lean Six Sigma for Supply Chain Management-2nd Edition, the Lean Six Sigma program became popular in the middle to late 1990s after General Electric embraced its philosophy and methods. That was almost 20 years ago, and much has changed relative to technology, data management, and data analysis. Whereas the initial focus of the Lean Six Sigma program was on manufacturing and isolated to work cells and machines, the potential for process improvement in the front office and across supply chains quickly became evident. Lean applications solve the most pressing issues of service processes through value identification, process simplification, standardization, and mistake-proofing. In our experience, approximately 80 percent of the process-improvement issues have been Lean applications. The remaining issues were resolved using statistical analysis and model building of various types. For some applications, the tools and methods of Lean Six Sigma are quickly becoming dated because of the rapid increases in data size, the differing types of data, and the underlying data structure. The need for additional tools and methods for defining, analyzing, and executing improvement projects is becoming apparent because the questions to be answered are sometimes complex.

 

The current tools and methods of Lean Six Sigma training leave unanswered complicated but important questions. Few Lean Six Sigma belts have the training, tools, or methods to work through big-data environments and analytics. New tools and methods require using different statistical software, large data repositories, and analytical sand boxes, conditioning different types of data, and using advanced statistical methods such as data mining. The newer requirements for big-data applications have become the acquisition, storage, searching, analysis, reporting, and visualization of data as well as their transfer between users of various types.

 

Big data consist of very large data sets and non-numeric data structures. Rather than using smaller data sets and merging them into single files, a single massive database is created to represent all associated data fields. This massive database is available in near real time within a data lake or analytical sandbox in which data refresh rates are more frequent than across several databases. The underlying data structure varies from numbers, text, video, and audio to other file and field formats. Big data methods will become a major enabler for future Lean Six Sigma deployments. Applications will include financial transactions, communications (including wireless networks), remote sensing (including software logs), video surveillance, and the numerous other data-collection devices, including cell phones, notebooks, computers, and sensors of various types.

 

Big-data structures and their disparate data types are searched to identify patterns to create patterns and models. Searching in these environments requires new tools and methods. In addition to file size and data structure, the resultant analyses are complex. In the past 30 years, specialized analytical methods have been created for structured, semi-structured, and unstructured data formats. Structured data are a common format used by belts for analyses e.g. Excel. Such data often require some transformations, but these are straightforward. Once converted into structured data format, conventional statistical methods familiar to Lean Six Sigma belts can be applied directly to complete an analysis or build models. Occasionally, there are requirements for transformation of semi-structured data into structured formats. Parsing of data fields is one common solution. However, semi-structured data cannot always be transformed into patterns or models without using newer methods. Examples include the parsing of text and numbers using specially designed   algorithms or searching for the number of times phases appear or their inter-relationships. Unstructured data have many formats. Examples include information contained in books, journals, documents, metadata, health records, audio, video, analog data, files, and unstructured text such as the body of an e-mail , webpage, or Word document.

 

In summary, big-data analytics requires an augmented set of methods and specialized software. These are driven by the data structure, its size, and the complicated business questions that need to be answered by an analyst. Historically, the Lean Six Sigma industry has used a limited number of software packages based on ease of use. In contrast, the software commonly used for big-data analytics include SAS, R, and similar enterprise-level tools. In addition, data-mining tools such as Weka are also used for analysis. Weka is an acronym for Waikato Environment for Knowledge Analysis developed by New Zealand’s University of Waikato. It is an extensive collection of data-processing, -conditioning, and -mining tools. Big data requires conditioning using automated rules and algorithms used to identify missing and incorrect data fields or other data issues. It is impossible to manually condition large databases. Specific methods include decision trees (including simple, linear, algebraic, deterministic, randomized, and related methods), Bayes’ theorem, regression (linear, nonlinear, and its many variations), distance and clustering methods, ensemble methods, instance-based learning, and many others. There is a long and unfamiliar list of tools and methods relative to those classically taught to belts.”


The need for big data analytical methods will grow in the coming years. Belts will need additional training in these methods to frame questions and obtain answers for increasingly large and complex process problems which will challenge organizations. The big data analytics market is expected to reach more than $100 billion in sales for 2015.

 


How should we apply Lean Six Sigma to service and product design?

 

In the twenty years, there have been differing opinions around  using Six Sigma to create and design products and services. These opinions claim time to market has been reduced and reliability increased using Design for Six Sigma (DFSS) whereas others argue innovation is stifled. Differing opinions are understandable.

 

First, in some applications, Define, Measure, Analyze, Improve, Control (DMAIC) methods were applied to design new products and services rather than improve current processes having chronic problems i.e. the original DMAIC intent. It is true DMAIC and DFSS share common tools and methods; but, the applications are different. DMAIC seeks to optimize one or two key process output variables (KPOVs) relative to key process input variables (KPIVs). In contrast, DFSS seeks to optimize many KPOVs. Second, DFSS has a greater focus on capturing the voice of the external rather than internal customer using advanced tools and methods such as Kano analysis, Pugh matrices, and a range of other analytical methods. These help capture “voice of” information. Third, in DFSS, the requirements mapping is from the external customer, through design features and functions into process requirements e.g. Quality Function Deployment (QFD).

 

How do DMAIC and DFSS methods help improve the design of products and services?  First Lean and DMAIC can be applied to understand the issues within a current design process. These include non-value adding steps and process waste. Almost any design process can be simplified, standardized and mistake proofed using a Lean and DMAIC approach to reduce cycle time, cost and improve quality.  DFSS methods should also be applied to the current design process to ensure. Examples include  effective identification of customer needs and value elements, analytical tools and methods for fact based decisions and alterative design comparisons, as well as robustness introduced using reliability and advanced statistical analyses. Organizations that are too busy to deploy DFSS, often repeat mistakes. The effective deployment of design for six sigma requires that its tools, methods  and examples be customized for each industry. Broad brushing them will only result in non-value adding activities and frustration.


Why do Lean Six Sigma deployments fail?

Twenty years ago when the first major Lean Six Sigma (LSS) deployments occurred outside of Motorola there were specific success criteria. The early adopter organizations followed the success criteria to increase productivity between 2% and 4%. Examples include Allied-Signal (now Honeywell) and General Electric (GE). But there were many others. The first consulting organizations, Six Sigma Academy (now SSA & Company), the George Group (now part of Accenture) and others (including Six Sigma Integration, Inc.) promoted use of the success criteria. But, as time passed, the Lean Six Sigma branding became diluted as the number of consulting organizations and poorly trained champions and belts proliferated.

The success criteria for LSS deployments are executive support as well as its integration and alignment through the various organizational functions e.g. human resources, information technology, finance and operations, the identification and prioritization of projects that add incremental value, the selection of the right belts (full time) and project execution using the LSS roadmaps and phased training (instructor or on-line) as well as mentoring.

The most successful deployments have been top-down rather than middle-in or bottom-up. Unfortunately, very few deployments are actually top-down. This results in lost productivity (as an opportunity cost) because organizations meet their goals in other ways.  Another way to think of these situations is that organizations compete through core competencies. Some organizations are successful through sales, marketing and design; others through operational efficiencies and others using different strategies. Only the best in class organizations (Honeywell, GE and others) push every function to be its best. As an example in organizations that are focused on sales, marketing and design, the back office operations are often inefficient (neglected).

Another reason for deployment failures is the project selection process. Productivity opportunities between 2% and 4% range occur only if projects are identified and prioritized by executive steering committees. These are usually called Operational Excellence (OPEX) committees. These OPEX committees’ review and control the projects within an organization. An effective project selection process also helps identify the black belts that need training to execute the prioritized projects. The goal should be fulltime black belts who have leadership potential. Part time black belts are a contributor to program failure and a symptom of lack of executive support as well as poor project selection.

In the past twenty years, there have been numerous discussions regarding the best way to train black belts. Instructor training is preferred by some and on-line (remote) training by others. Effective deployments can work well using either training strategy if black belts are effectively mentored through their project phases by consultants or experienced master black belts.

Training content should also be customized for a specific industry. As an example, experimental design methods are useful for research and manufacturing, operations research for logistics and advanced statistical modeling for marketing, big data analytics and other applications. Belts should learn the right tools and methods to improve their processes. Training content is especially important for Design for Six Sigma (DFSS) training applications because different industries use differ tools and methods for design of products and services. As an example, designing a bridge requires different methods than designing an accounts payable process.

A Lean Six Sigma deployment can be improved using these known success factors. It’s a robust process improvement methodology. Regardless of your deployment’s status, you will always know what needs to be done to improve it. Most deployment issues are associated with a lack of organizational alignment or support.


How can Customer Experience Mapping help improve your process?

 

From my book titled, Lean Six Sigma for Supply Chain Management 2nd Edition, obtaining customer feedback is often done through surrogates such as sales and marketing personnel or third parties. Seldom do the people responsible for process improvement have access to direct customer feedback. This resultant information is not actionable. However, customers are inundated with requests for feedback from many sources. A large supplier may use several surveys requesting feedback from the same customers too frequently. There may also be issues with phasing of questions, their delivery, or the dilution of sample sizes over  products, services, and respondents. As a result, there is a heavy reliance on information gained passively from product returns, warranty issues, and other feedback systems rather than direct customer feedback.

 

A problem with surveys (indirect feedback as opposed to in person surveys)  or collecting information passively is that the customer message becomes diluted. The analytical results will not be useful for driving process-improvement actions. But, imagine using a method that enables a process-improvement team to work directly with customers to identify key touch points and performance gaps. Customer experience mapping (CEM) is a joint supplier-customer workshop, similar to a Kaizen event, in which the key touch points between organizations are mapped to identify gaps. Touch points are embedded within the higher level steps associated with the sale, purchase, delivery, and use of products and services. A typical customer experience map is built with these steps in sequence. Beneath each step are one or more touch points colored-coded and marked up to identify gaps, moments of truth, and other evaluation or prioritization criteria.

 

An advantage of the CEM approach is that customer needs and expectations become clearly understood through mutual interaction and agreement. CEM also becomes a long-term road map or model from which to continuously improve the customer experience. This information also can be integrated into a supplier’s formal "voice of" programs. For example, voice-of-customer (VOC), voice-of-partner (VOP), and the voice-of-field  (VOF). These “voice-of” programs capture metrics that measure customer relationships from perspectives of loyalty and transaction experience using interviews, electronic surveys, and analyses of returns, allowances, warranty information, and other methods discussed earlier. An effective CEM program helps validate information collected by passive data-collection methods. Ideally, in aggregate, the information will enable an organization to effectively focus its continuous-improvement efforts. to improve Total Customer Experience (TCE).

 


What are some new trends for Lean Six Sigma Supply-Chain improvement?

 

From my book titled, Lean Six Sigma for Supply Chain Management-2nd Edition, in the past 20 years, technology has made it possible to design, produce, sell, manage, distribute, and service customers using centralized, decentralized, or hybrid organizational models. This capability is enabled by accessing common enterprise software platforms and databases from anywhere and at any time. It has helped create entirely new supply chains and processes with unique performance characteristics. There are new terms describing these technological changes, for example, big data, cloud computing, virtualization, and predictive analytics, to name a few. The tools and methods of Lean Six Sigma need to keep up with these newer technology-based improvement opportunities. In addition to technological changes, there has been an increased focus on environmental sustainability, energy efficiency, and human rights. Sustainability activities include reducing an organization’s carbon footprint, reducing its e-waste, and minimizing adverse climate-change effects. These are major initiatives championed by leading edge organizations. Recently, the term conflict resource was created to describe resources that imperil human life or the environment through their acquisition and sale. These new trends also reflect a new focus for products, services and process improvement.

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